Nanocellulose, a Versatile Green Platform: From Biosources to Materials and Their Applications
Bibliographic record
Abstract
With increasing environmental and ecological concerns due to the use of petroleum-based chemicals and products, the synthesis of fine chemicals and functional materials from natural resources is of great public value. Nanocellulose may prove to be one of the most promising green materials of modern times due to its intrinsic properties, renewability, and abundance. In this review, we present nanocellulose-based materials from sourcing, synthesis, and surface modification of nanocellulose, to materials formation and applications. Nanocellulose can be sourced from biomass, plants, or bacteria, relying on fairly simple, scalable, and efficient isolation techniques. Mechanical, chemical, and enzymatic treatments, or a combination of these, can be used to extract nanocellulose from natural sources. The properties of nanocellulose are dependent on the source, the isolation technique, and potential subsequent surface transformations. Nanocellulose surface modification techniques are typically used to introduce either charged or hydrophobic moieties, and include amidation, esterification, etherification, silylation, polymerization, urethanization, sulfonation, and phosphorylation. Nanocellulose has excellent strength, high Young's modulus, biocompatibility, and tunable self-assembly, thixotropic, and photonic properties, which are essential for the applications of this material. Nanocellulose participates in the fabrication of a large range of nanomaterials and nanocomposites, including those based on polymers, metals, metal oxides, and carbon. In particular, nanocellulose complements organic-based materials, where it imparts its mechanical properties to the composite. Nanocellulose is a promising material whenever material strength, flexibility, and/or specific nanostructuration are required. Applications include functional paper, optoelectronics, and antibacterial coatings, packaging, mechanically reinforced polymer composites, tissue scaffolds, drug delivery, biosensors, energy storage, catalysis, environmental remediation, and electrochemically controlled separation. Phosphorylated nanocellulose is a particularly interesting material, spanning a surprising set of applications in various dimensions including bone scaffolds, adsorbents, and flame retardants and as a support for the heterogenization of homogeneous catalysts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".